# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
from core.utils import score

def batch_pix_accuracy(output, target):
    """PixAcc"""
    # inputs are numpy array, output 4D, target 3D
    predict = torch.argmax(output.float(), 1) + 1.
    target = target.float() + 1.

    pixel_labeled = torch.sum(target > 0).item()
    pixel_correct = torch.sum((predict == target) * (target > 0)).item()
    assert pixel_correct <= pixel_labeled, "Correct area should be smaller than Labeled"
    return pixel_correct, pixel_labeled
score.batch_pix_accuracy = batch_pix_accuracy
